Socioeconomic factors and Rat sightings

Data Cleaning:

Steps

clean names and select necessary variables
rename the variables
drop any rows with missing observations
filter out the second row (contains comments about the columns)
join all the datasets
drop any rows with missing observations
create new column, id, using the geo id to help with matching to zipcodes # idk if need to do this actually
filter to only keep counties relevant to NYC
create new column for zipcode by matching to id

Crowding data

less_than_1 = Occupied housing units with 1.00 or less occupants per room
bet_1to1.5 = Occupied housing units with 1.01 to 1.50 occupants per room
more_than_1.5 = Occupied housing units with 1.51 or more occupants per room

crowding18 =
  read_csv("Data/crowding/2018_crowding.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, name, s2501_c01_006e, s2501_c01_007e, s2501_c01_008e) |> 
  rename(
    less_than_1_y18   = s2501_c01_006e,
    bet_1to1.5_y18    = s2501_c01_007e,
    more_than_1.5_y18 = s2501_c01_008e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_than_1_y18","bet_1to1.5_y18","more_than_1.5_y18"), as.numeric)

## Rows: 4919 Columns: 458
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (458): GEO_ID, NAME, S2501_C01_001E, S2501_C01_001M, S2501_C01_002E, S25...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

crowding19 =
  read_csv("Data/crowding/2019_crowding.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s2501_c01_006e, s2501_c01_007e, s2501_c01_008e) |> 
  rename(
    less_than_1_y19   = s2501_c01_006e,
    bet_1to1.5_y19    = s2501_c01_007e,
    more_than_1.5_y19 = s2501_c01_008e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_than_1_y19","bet_1to1.5_y19","more_than_1.5_y19"), as.numeric)

## Rows: 4919 Columns: 458
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (458): GEO_ID, NAME, S2501_C01_001E, S2501_C01_001M, S2501_C01_002E, S25...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

crowding20 =
  read_csv("Data/crowding/2020_crowding.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s2501_c01_006e, s2501_c01_007e, s2501_c01_008e) |> 
  rename(
    less_than_1_y20   = s2501_c01_006e,
    bet_1to1.5_y20    = s2501_c01_007e,
    more_than_1.5_y20 = s2501_c01_008e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_than_1_y20","bet_1to1.5_y20","more_than_1.5_y20"), as.numeric)

## Rows: 5412 Columns: 458
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (458): GEO_ID, NAME, S2501_C01_001E, S2501_C01_001M, S2501_C01_002E, S25...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

crowding21 =
  read_csv("Data/crowding/2021_crowding.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s2501_c01_006e, s2501_c01_007e, s2501_c01_008e) |> 
  rename(
    less_than_1_y21   = s2501_c01_006e,
    bet_1to1.5_y21    = s2501_c01_007e,
    more_than_1.5_y21 = s2501_c01_008e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_than_1_y21","bet_1to1.5_y21","more_than_1.5_y21"), as.numeric)

## Rows: 5412 Columns: 458
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (458): GEO_ID, NAME, S2501_C01_001E, S2501_C01_001M, S2501_C01_002E, S25...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

crowding_10s = 
  full_join(crowding18, crowding19, by = "geo_id")

crowding_20s = 
  full_join(crowding20, crowding21, by = "geo_id")

crowding_all =
  full_join(crowding_10s, crowding_20s, by = "geo_id") |> 
  drop_na() |> 
  mutate(
    id = str_sub(geo_id, 10),
    county = str_extract(name, "(?<=,)[^,]+(?=,)")
  ) |> 
  filter(county %in% c(" Kings County"," Bronx County"," Queens County"," Richmond County"," New York County"))
  
# can I make a map using geo id?
# my time plot cuts the year in half
# time plot title is cut off

Education Data

less_9 = Population 25 years and over - Less than 9th grade
no_hs_diploma = Population 25 years and over - 9th to 12th grade, no diploma
hs_grad = Population 25 years and over - High school graduate (includes equivalency)
some_college = Population 25 years and over - Some college, no degree
associate = Population 25 years and over - Associate’s degree
bachelor = Population 25 years and over - Bachelor’s degree
graduate = Population 25 years and over - Graduate or professional degree
hs_or_less = Population 25 years and over with HS equivalent education or less
college = Population 25 years and over with at least some college education

edu18 =
  read_csv("Data/edu/edu_2018.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, name, s1501_c01_007e, s1501_c01_008e, s1501_c01_009e, s1501_c01_010e, s1501_c01_011e, 
         s1501_c01_012e, s1501_c01_013e) |> 
  rename(
    less_9_y18        = s1501_c01_007e,
    no_hs_diploma_y18 = s1501_c01_008e,
    hs_grad_y18       = s1501_c01_009e,
    some_college_y18  = s1501_c01_010e,
    associate_y18     = s1501_c01_011e,
    bachelor_y18      = s1501_c01_012e,
    graduate_y18      = s1501_c01_013e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_9_y18","no_hs_diploma_y18","hs_grad_y18","some_college_y18","associate_y18",
              "bachelor_y18","graduate_y18"), as.numeric) |> 
  mutate(
    hs_or_less_y18 = rowSums(across(c(less_9_y18,no_hs_diploma_y18,hs_grad_y18))),
    college_y18    = rowSums(across(c(some_college_y18,associate_y18,bachelor_y18,graduate_y18)))
  )

## Rows: 4919 Columns: 770
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (770): GEO_ID, NAME, S1501_C01_001E, S1501_C01_001M, S1501_C01_002E, S15...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

edu19 =
  read_csv("Data/edu/edu_2019.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1501_c01_007e, s1501_c01_008e, s1501_c01_009e, s1501_c01_010e, s1501_c01_011e, 
         s1501_c01_012e, s1501_c01_013e) |> 
  rename(
    less_9_y19        = s1501_c01_007e,
    no_hs_diploma_y19 = s1501_c01_008e,
    hs_grad_y19       = s1501_c01_009e,
    some_college_y19  = s1501_c01_010e,
    associate_y19     = s1501_c01_011e,
    bachelor_y19      = s1501_c01_012e,
    graduate_y19      = s1501_c01_013e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_9_y19","no_hs_diploma_y19","hs_grad_y19","some_college_y19","associate_y19",
              "bachelor_y19","graduate_y19"), as.numeric) |> 
  mutate(
    hs_or_less_y19 = rowSums(across(c(less_9_y19,no_hs_diploma_y19,hs_grad_y19))),
    college_y19    = rowSums(across(c(some_college_y19,associate_y19,bachelor_y19,graduate_y19)))
  )

## Rows: 4919 Columns: 770
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (770): GEO_ID, NAME, S1501_C01_001E, S1501_C01_001M, S1501_C01_002E, S15...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

edu20 =
  read_csv("Data/edu/edu_2020.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1501_c01_007e, s1501_c01_008e, s1501_c01_009e, s1501_c01_010e, s1501_c01_011e, 
         s1501_c01_012e, s1501_c01_013e) |> 
  rename(
    less_9_y20        = s1501_c01_007e,
    no_hs_diploma_y20 = s1501_c01_008e,
    hs_grad_y20       = s1501_c01_009e,
    some_college_y20  = s1501_c01_010e,
    associate_y20     = s1501_c01_011e,
    bachelor_y20      = s1501_c01_012e,
    graduate_y20      = s1501_c01_013e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_9_y20","no_hs_diploma_y20","hs_grad_y20","some_college_y20","associate_y20",
              "bachelor_y20","graduate_y20"), as.numeric) |> 
  mutate(
    hs_or_less_y20 = rowSums(across(c(less_9_y20,no_hs_diploma_y20,hs_grad_y20))),
    college_y20    = rowSums(across(c(some_college_y20,associate_y20,bachelor_y20,graduate_y20)))
  )

## Rows: 5412 Columns: 770
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (770): GEO_ID, NAME, S1501_C01_001E, S1501_C01_001M, S1501_C01_002E, S15...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

edu21 =
  read_csv("Data/edu/edu_2021.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1501_c01_007e, s1501_c01_008e, s1501_c01_009e, s1501_c01_010e, s1501_c01_011e, 
         s1501_c01_012e, s1501_c01_013e) |> 
  rename(
    less_9_y21        = s1501_c01_007e,
    no_hs_diploma_y21 = s1501_c01_008e,
    hs_grad_y21       = s1501_c01_009e,
    some_college_y21  = s1501_c01_010e,
    associate_y21     = s1501_c01_011e,
    bachelor_y21      = s1501_c01_012e,
    graduate_y21      = s1501_c01_013e
  ) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("less_9_y21","no_hs_diploma_y21","hs_grad_y21","some_college_y21","associate_y21",
              "bachelor_y21","graduate_y21"), as.numeric) |> 
  mutate(
    hs_or_less_y21 = rowSums(across(c(less_9_y21,no_hs_diploma_y21,hs_grad_y21))),
    college_y21    = rowSums(across(c(some_college_y21,associate_y21,bachelor_y21,graduate_y21)))
  )

## Rows: 5412 Columns: 770
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (770): GEO_ID, NAME, S1501_C01_001E, S1501_C01_001M, S1501_C01_002E, S15...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

edu_10s = 
  full_join(edu18, edu19, by = "geo_id")

edu_20s = 
  full_join(edu20, edu21, by = "geo_id")

edu_all =
  full_join(edu_10s, edu_20s, by = "geo_id") |> 
  drop_na() |> 
  mutate(
    id = str_sub(geo_id, 10),
    county = str_extract(name, "(?<=,)[^,]+(?=,)")
  ) |> 
  filter(county %in% c(" Kings County"," Bronx County"," Queens County"," Richmond County"," New York County"))

Poverty Data

below_poverty = % below poverty level among population for whom poverty status is determined

poverty18 =
  read_csv("Data/poverty/2018_poverty.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, name, s1701_c03_001e) |> 
  rename(
    below_poverty_y18 = s1701_c03_001e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("below_poverty_y18"), as.numeric)

## Rows: 4919 Columns: 368
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (368): GEO_ID, NAME, S1701_C01_001E, S1701_C01_001M, S1701_C01_002E, S17...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

poverty19 =
  read_csv("Data/poverty/2019_poverty.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1701_c03_001e) |> 
  rename(
    below_poverty_y19 = s1701_c03_001e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("below_poverty_y19"), as.numeric)

## Rows: 4919 Columns: 368
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (368): GEO_ID, NAME, S1701_C01_001E, S1701_C01_001M, S1701_C01_002E, S17...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

poverty20 =
  read_csv("Data/poverty/2020_poverty.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1701_c03_001e) |> 
  rename(
    below_poverty_y20 = s1701_c03_001e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("below_poverty_y20"), as.numeric)

## Rows: 5412 Columns: 368
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (368): GEO_ID, NAME, S1701_C01_001E, S1701_C01_001M, S1701_C01_002E, S17...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

poverty21 =
  read_csv("Data/poverty/2021_poverty.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, s1701_c03_001e) |> 
  rename(
    below_poverty_y21 = s1701_c03_001e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("below_poverty_y21"), as.numeric)

## Rows: 5412 Columns: 374
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (374): GEO_ID, NAME, S1701_C01_001E, S1701_C01_001M, S1701_C01_002E, S17...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

poverty_10s = 
  full_join(poverty18, poverty19, by = "geo_id")

poverty_20s = 
  full_join(poverty20, poverty21, by = "geo_id")

poverty_all =
  full_join(poverty_10s, poverty_20s, by = "geo_id") |> 
  drop_na() |> 
  mutate(
    id = str_sub(geo_id, 10),
    county = str_extract(name, "(?<=,)[^,]+(?=,)")
  ) |> 
  filter(county %in% c(" Kings County"," Bronx County"," Queens County"," Richmond County"," New York County"))

Vacancy Data

total_home = total number of properties for residential purposes
vacant = total number of vacant properties
prop_vacant = proportion of vacant properties in a census tract

vacancy18 =
  read_csv("Data/vacancy/2018_vacancy.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, name, b25004_001e, b25004_008e) |> 
  rename(
    total_home_y18 = b25004_001e,
    vacant_y18     = b25004_008e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("total_home_y18","vacant_y18"), as.numeric) |> 
  mutate(prop_vacant_y18 = vacant_y18/total_home_y18)

## Rows: 4919 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (18): GEO_ID, NAME, B25004_001E, B25004_001M, B25004_002E, B25004_002M, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

vacancy19 =
  read_csv("Data/vacancy/2019_vacancy.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, b25004_001e, b25004_008e) |> 
  rename(
    total_home_y19 = b25004_001e,
    vacant_y19     = b25004_008e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("total_home_y19","vacant_y19"), as.numeric) |> 
  mutate(prop_vacant_y19 = vacant_y19/total_home_y19)

## New names:
## Rows: 4919 Columns: 19
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (18): GEO_ID, NAME, B25004_001E, B25004_001M, B25004_002E, B25004_002M, ... lgl
## (1): ...19
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...19`

vacancy20 =
  read_csv("Data/vacancy/2020_vacancy.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, b25004_001e, b25004_008e) |> 
  rename(
    total_home_y20 = b25004_001e,
    vacant_y20     = b25004_008e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |> 
  mutate_at(c("total_home_y20","vacant_y20"), as.numeric) |> 
  mutate(prop_vacant_y20 = vacant_y20/total_home_y20)

## New names:
## Rows: 5412 Columns: 19
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (18): GEO_ID, NAME, B25004_001E, B25004_001M, B25004_002E, B25004_002M, ... lgl
## (1): ...19
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...19`

vacancy21 =
  read_csv("Data/vacancy/2021_vacancy.csv", col_names = TRUE) |> 
  janitor::clean_names() |> 
  select(geo_id, b25004_001e, b25004_008e) |> 
  rename(
    total_home_y21 = b25004_001e,
    vacant_y21     = b25004_008e) |> 
  drop_na() |> 
  filter(!row_number() %in% c(1)) |>  
  mutate_at(c("total_home_y21","vacant_y21"), as.numeric) |> 
  mutate(prop_vacant_y21 = vacant_y21/total_home_y21)

## New names:
## Rows: 5412 Columns: 19
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (18): GEO_ID, NAME, B25004_001E, B25004_001M, B25004_002E, B25004_002M, ... lgl
## (1): ...19
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...19`

vacancy_10s = 
  full_join(vacancy18, vacancy19, by = "geo_id")

vacancy_20s = 
  full_join(vacancy20, vacancy21, by = "geo_id")

vacancy_all =
  full_join(vacancy_10s, vacancy_20s, by = "geo_id") |> 
  drop_na() |> 
  mutate( 
    id = str_sub(geo_id, 10),
    county = str_extract(name, "(?<=,)[^,]+(?=,)")
    ) |> 
  filter(county %in% c(" Kings County"," Bronx County"," Queens County"," Richmond County"," New York County"))

It should be noted that the dataset included information about other types of properties but were unoccupied such as rental properties, those listed for sale, properties sold but unoccupied, those designated for migrant workers, and residences that were used occasionally/seasonally/recreationally. The only variable considered in this analysis was vacant properties however it is likely that residences that go unoccupied for several months can become nests for the rat population, increasing sightings in those areas. A more thorough and in-depth analysis should include the other variables provided in the dataset.

In general, might be losing information with the variables chosen for analysis. During data collection, the variables were further subsetted into age groups and race/ethnicity designations. However for this analysis, the general estimates were taken for the population as a whole in NYC.

When importing data from 2018 and 2019, there were 4918 census tracts and when importing data from 2020 and 2021, there were 5411 census tracts. The datasets for each year were joined together by geography id to only retain census tracts that were consistent across the years. There may have been adjustments or redfining areas but that could affect analysis.

Plots:

crowding_time = crowding_all |> 
  pivot_longer(less_than_1_y18:more_than_1.5_y21,
               names_to = "occupancy",
               values_to = "count") |> 
  mutate(year     = str_sub(occupancy, -3),
         category = str_sub(occupancy, end=-5)) |> 
  mutate(
    year = case_match(
      year,
      "y18" ~ 2018,
      "y19" ~ 2019,
      "y20" ~ 2020,
      "y21" ~ 2021
    )
  ) |> 
  filter(category == "more_than_1.5") |> 
  group_by(county,year) |> 
  summarise(mean = mean(count)) |> 
  plot_ly(x = ~year, y = ~mean, color = ~county, type = "scatter", mode = "lines", 
          colors = "viridis") |> 
  layout(title = "Timeseries of Average Number of Housing Units with 1.51 or More Occupants/Room by Borough in NYC",
         plot_bgcolor = "e5ecf6", 
         xaxis = list(autotypenumbers = 'strict', title = 'Year'), 
         yaxis = list(title = 'Average Number of Housing Units'))

## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.

crowding_time

Analysis

edu_time = edu_all |> 
  pivot_longer(less_9_y18:college_y21,
               names_to = "education_level",
               values_to = "count") |> 
  mutate(year     = str_sub(education_level, -3),
         category = str_sub(education_level, end=-5)) |> 
  mutate(
    year = case_match(
      year,
      "y18" ~ 2018,
      "y19" ~ 2019,
      "y20" ~ 2020,
      "y21" ~ 2021
    )
  ) |> 
  filter(category == "hs_or_less") |> 
  group_by(county,year) |> 
  summarise(mean = mean(count)) |> 
  plot_ly(x = ~year, y = ~mean, color = ~county, type = "scatter", mode = "lines", 
          colors = "viridis") |> 
  layout(title = "Timeseries of Average Number of Individuals with HS or less Education Level by Borough in NYC",
         plot_bgcolor = "e5ecf6", 
         xaxis = list(autotypenumbers = 'strict', title = 'Year'), 
         yaxis = list(title = 'Average Number of Individuals'))

## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.

edu_time

Analysis

poverty_time = poverty_all |> 
  pivot_longer(below_poverty_y18:below_poverty_y21,
               names_to = "poverty_level",
               values_to = "count") |> 
  mutate(year = str_sub(poverty_level, -3)) |> 
  mutate(
    year = case_match(
      year,
      "y18" ~ 2018,
      "y19" ~ 2019,
      "y20" ~ 2020,
      "y21" ~ 2021
    )
  ) |> 
  group_by(county,year) |> 
  summarise(mean = mean(count)) |> 
  plot_ly(x = ~year, y = ~mean, color = ~county, type = "scatter", mode = "lines", 
          colors = "viridis") |> 
  layout(title = "Timeseries of Average Number of Housing Units Below the Poverty Level by Borough in NYC",
         plot_bgcolor = "e5ecf6", 
         xaxis = list(autotypenumbers = 'strict', title = 'Year'), 
         yaxis = list(title = 'Average Number of Households Below Poverty Level'))

## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.

poverty_time

Analysis

vacancy_time = vacancy_all |> 
  pivot_longer(total_home_y18:prop_vacant_y21,
               names_to = "vacancy",
               values_to = "count") |> 
  mutate(year     = str_sub(vacancy, -3),
         category = str_sub(vacancy, end=-5)) |> 
  mutate(
    year = case_match(
      year,
      "y18" ~ 2018,
      "y19" ~ 2019,
      "y20" ~ 2020,
      "y21" ~ 2021
    )
  ) |> 
  group_by(county,year) |> 
  summarise(mean = mean(count)) |> 
  plot_ly(x = ~year, y = ~mean, color = ~county, type = "scatter", mode = "lines", 
          colors = "viridis") |> 
  layout(title = "Timeseries of Average Proportion of Vacant Properties by Borough in NYC",
         plot_bgcolor = "e5ecf6", 
         xaxis = list(autotypenumbers = 'strict', title = 'Year'), 
         yaxis = list(title = 'Average Proportion'))

## `summarise()` has grouped output by 'county'. You can override using the
## `.groups` argument.

vacancy_time

Analysis

make distribution plots (if possible) otherwise do barcharts comparing the different boroughs for variable of interest for 2021

explain how each of these variables may be related to rat sightings and increased presence, go back to the rat paper possibly compare to the rat distribution

upload everything to shared repository

Socioeconomic factors and Rat sightings

Nandini M

2023-12-03

Data Cleaning: